Impact Of Meteorological Parameters Research Articles

Extreme sea levels and floods: the case study of Klaipėda City, Lithuania

Klaipėda City is located in the Southeast Baltic Sea region, where sea level rise has been observed for decades. The Klaipėda Strait, which separates the Baltic Sea and the Curonian Lagoon, where the port is located, is also more prone to sudden extreme changes in water levels, usually caused by windstorms. Extreme sea level changes pose a threat to port operations, technical structures, city residents, buildings, and infrastructure. Fluctuations in sea levels also affect the water level of the Danė River, which enters the Klaipėda Strait and divides the city into two parts. Therefore, this study aims to determine the past extreme sea level events and their influence on floods in the Danė River within Klaipėda City from 1961 to 2022. For this, the impact of meteorological parameters caused dangerous sea level rises in the Klaipėda Strait, and the following rise of the Danė River was studied. The results show that the annual mean, annual mean maximum, and annual mean minimum water levels in the Klaipėda Strait increased from 1961 to 2022. Also, the number of events where the water level in the Klaipėda Strait was ≥ 100 cm in the Baltic Elevation System was increasing. The increasing frequency of extreme water level events in the Klaipėda Strait puts urban areas at greater risk from Danė River compound floods.

Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques.

The daily soil radon activity has been measured continuously over a year with BARASOL BMC2 probe at a measuring site of Jadavpur University Campus in Kolkata, India. The dependency of soil radon activity with different atmospheric parameters such as soil temperature, soil pressure, humidity, air temperature, and rainfall has been also analyzed. The whole study period is divided in four seasons as proposed by the Indian Meteorological Department (IMD). Minimum soil radon level has been observed during the winter season (December-February). On the other hand, higher soil radon level has been observed both for summer and monsoon. Except soil pressure, all other variables have shown positive correlation with soil radon activity. Among five variables, soil temperature has been the most significant variable in terms of correlation with soil radon level whereas maximum humidity has been the least significant correlated variable. It has been observed that considerable reduction of soil radon level occurred after four heavy rainfall events during the study period. The combined effect of these multi-parameters on soil radon gas has been evaluated using machine learning methods like principal component regression (PCR), support vector regression (SVR), random forest regression (RF), and gradient boosting machine (GBM). In terms of performances, RF and GBM have performed much better than SVR and PCR. More robust and consistent results have been obtained for GBM during both training and testing periods.

The impact of meteorological parameters on the number of applications to the emergency department with acute urticaria: A retrospective study.

The study aimed to investigate the relationship between the patients who applied to the emergency department with acute urticarial and meteorological factors and determine the associated weather conditions. The relationship between acute urticaria patients who applied to the emergency department of a training and research hospital in a city with high altitude and continental climate characteristics in the eastern part of Turkey between January 2019 and December 2019 and meteorological data was evaluated retrospectively. The necessary data for the weather were obtained from the data of the Erzurum Meteorology Directorate, and the patient data were obtained from the hospital information management system and patient files. The meteorological data of the patients at the date of admission and the characteristics of the patients at that date were compared. The study identified 691 patients who applied to the emergency department diagnosed with urticaria in 2019. According to the seasons, it was observed that the patients applied most frequently in the summer months [n = 239; 34.6%]. In the univariable regression model, which was created by taking the values of weather events as a reference on the days when the urticaria presentation was not observed (Group I), it was determined that every 1-hour increase in the sunshine hour increased the probability of urticaria by 7.4% (p = 0.018). When the meteorological parameters on the days without urticaria (Group I) and the days with urticaria presentation (Group II) were compared, the sunshine hours were 7.9 (4.0-10.6) hours in Group II and 6.65 (3.3-8.85) hours in Group I (p = 0.001). Regarding relative humidity, higher humidity rates were observed in Group I compared to Group II (p = 0.009). In terms of mean temperature, daily maximum, and minimum temperature, higher temperature rates were detected in Group II (p<0.001). A relationship was determined between urticaria attacks and relative humidity and daily maximum and minimum temperature in patients who applied to the emergency department with acute urticaria.

Generation of Typical Meteorological Sequences to Simulate Growth and Production of Biological Systems

Numerical simulation applied to agriculture or wastewater treatment (WWT) is a complementary tool to understand, a priori, the impact of meteorological parameters on productivity under limiting environmental conditions or even to guide investments towards other more relevant circular economic objectives. This work proposes a new methodology to calculate Typical Meteorological Sequences (TMS) that could be used as input data to simulate the growth and productivity of photosynthetic organisms in different biological systems, such as a High-Rate Algae Pond (HRAP) for WWT or in agriculture for crops. The TMS was established by applying Finkelstein-Schafer statistics and represents the most likely meteorological sequence in the long term for each meteorological season. In our case study, 18 locations in the Madrid (Spain) region are estimated depending on climate conditions represented by solar irradiance and temperature. The parameters selected for generating TMS were photosynthetically active radiation, solar day length, maximum, minimum, mean, and temperature range. The selection of potential sequences according to the growth period of the organism is performed by resampling the available meteorological data, which, in this case study, increases the number of candidate sequences by 700%.

The Impact of Meteorological Parameters and Seasonal Changes on Reporting Patients with Selected Cardiovascular Diseases to Hospital Emergency Departments: A Pilot Study

(1) Background: So far, research results have confirmed the relationship between heat and cold stress, the fluctuations in atmospheric pressure and high relative humidity, and the vulnerability of patients with so-called "weather-dependent" diseases which could lead to death. This study aimed to determine the meteorological parameters, their interactions, and the seasonal changes of the most significant factors in predicting the number of patients reporting to the Emergency Departments (EDs) in Poznań (Poland) during 2019. (2) Methods: The analysis included the meteorological parameters and data of 3606 patients diagnosed with essential or complicated arterial hypertension, myocardial infarction, chronic ischemic heart disease, and ischemic or unspecified stroke by the International Classification of Diseases (ICD-10). The meteorological data (days per week and seasonal data) were used to build a linear regression model to assess the changes in the daily number of reporting patients. The input data for the final model were selected based on the principal component analysis (PCA), and built for each delay and acceleration (reporting up to 3 days before the change or up to 3 days after the change of the meteorological parameter). (3) Results: A significantly lower number of reports was observed during weekends compared to working days (standardised b = -0.254, p-value < 0.0001) and three days before the maximum daily air temperature in the spring and summer period (standardised b = -0.748, p-value < 0.0001), while two days after the increase in the daily amplitude of atmospheric pressure (standardised b = 0.116, p-value = 0.0267), and also on the day of occurrence of the unfavourable interdiurnal air temperature change, an increase in the number of patients was noted (standardised b = 0.115, p-value = 0.0186). The changes in the last two parameters were statistically insignificant. Based on the obtained results, the negative impact of the changes in the meteorological conditions on the number of reports to the EDs in Poznań was determined.

Aerosol Optical Depth Spatial and Temporal Variability Using Satellite Data Over Indian Major Cities

Introduction: The study’s main aim is to investigate the long-term variation of Aerosol Optical Depth (AOD). It also aims to show the relationship between meteorological parameters. This study evaluates long-term (2010 to 2021) special and temporal changes over major Indian regions using satellite-based data from NASA’s Terra Satellite.&#x0D; Materials and Methods: This study was carried out during 2010-2021 using MODIS data for long-term analysis. Variation of AOD with meteorological parameters was also performed to show the impact of these parameters (Temperature and Relative Humidity) on AOD by the MERRA-II model.&#x0D; Results: Based on Terra AOD data, in all the studied regions, especially the eastern region (Kolkata), the mean AOD was high (0.9-1.2). In the western region (Mumbai, Ahmedabad, and Surat), the mean AOD was low to moderate during all seasons. Furthermore, the impact of meteorological parameters on AOD shows significant variation in average annual AOD in Kolkata (0.70 ± 0.09), while other regions reported lower than average AOD values during the study period. Mumbai and Surat had average AODs (0.44 ± 0.13), (0.45 ± 0.14), while Ahmedabad and Jaipur reported average AOD of 0.45 ± 0.14 and 0.23 ± 0.11 during the study period, respectively.&#x0D; Conclusion: Generally, AOD values vary from season to season due to aerosol’s optical and microphysical properties being affected by meteorological conditions and surface albedo. This study examined the spatial and temporal distribution of AOD over five major Indian cities.

Thermal circulation affects Black Vulture Coragyps atratus soaring behaviour in the vicinity of two airports in south-east Brazil.

The growth of air traffic all over the world faces an increasing risk of aircraft collisions with birds. The Black Vulture (Coragyps atratus), being a large soaring species living across the Americas, presents a serious hazard to airplanes. The reduction of this threat without harm to birds is a complex task requiring thorough investigations of their behaviour. Various natural and anthropogenic factors can affect the distribution of soaring Black Vultures in the surrounds of airports. Here we focus on the impact of meteorological parameters (i.e. air temperature, relative humidity, wind speed and atmospheric pressure) on Black Vulture flight at two study sites. In the course of one year, from September 2012 to August 2013, we surveyed with binoculars the soaring activity of Black Vultures around Amarais and Presidente Prudente airports in the southeast of Brazil. The study areas were characterized by tropical climatic conditions with mild dry winter and hot rainy summer. We found that the frequency of soaring flights depended on the wind speed, a proxy of the strength of thermals (upward air flows). This finding is consistent with habits of Black Vultures using thermals for soaring. In contrast, air temperature, air humidity and atmospheric pressure did not affect their activity. We also showed that the different seasons affected the birds’ behaviour, but the degree of their influence depended on the level of anthropogenic pressure in the soaring terrain. Two contrasting types of daily soaring activity appeared: a plateau-like trend and a tendency of two peaks at morning and afternoon with a pronounced drop between them. Our findings can be used to inform methods to reduce the risk of collisions between aircraft and Black Vultures at our study sites, and our analytical approach could be applied elsewhere.

Studies of the impact of environmental conditions and varietal features of sweet cherry on the accumulation of vitamin C in fruits by using the regression analysis method

The accumulation of vitamin C in sweet cherry fruits depends on the variety and environmental conditions. The aim of our research was to substantiate the rate of impact of weather factors as well as of varietal features on vitamin C accumulation in sweet cherry fruits. The varieties ʹKazkaʹ and ʹZabutaʹ, ʹKordiaʹ and ʹMirazhʹ were chosen as the best ones from among 33 varieties of early, medium and late term of ripening (7.31–10.67 mg 100 g-1) according to the average content of vitamin C in sweet cherry fruits. The studies found that the environmental conditions of the research years had the largest impact on the vitamin C content in the fruits of late and early ripening varieties, and in the fruits of medium ripening variety the vitamin C amount depended on the varietal features. The practicability of forecasting of vitamin C content in sweet cherry fruits on the average indices for a group of early and late maturity varieties, but not separately for every pomological variety, has been proven. For the medium ripening variety this index can be forecasted within each pomological variety. The models of dependence of vitamins C accumulation on the impact of meteorological parameters were evaluated on the basis of the principle components analysis and the least square method.

Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization

To evaluate the effectiveness of air pollution control policies, trend analysis of the air pollutants is often performed. However, trend analysis of air pollutants over multiple years is complicated by the fact that changes in meteorology over time can also affect the levels of air pollutants in addition to changes in emissions or atmospheric chemistry. To decouple the meteorological effect, this study performed a trend analysis of the hourly fine particulate matter (PM2.5) observed at an urban background site in Xi'an city over 5 years from 2015 to 2019 using the machine learning algorithm. As a novel way of meteorological normalization, the meteorological parameters were used as constant input for 5 consecutive years. In this way, the impact of meteorological parameters was excluded, providing insights into the “real” changes in PM2.5 due to changes in emission strength or atmospheric chemistry. After meteorological normalization, a decreasing trend of −3.3 % year−1 (−1.9 μg m−3 year−1) in PM2.5 was seen, instead of −4.4 % year−1 from direct PM2.5 observation. Assuming the rate of −1.9 μg m−3 year−1 were kept constant for the next few decades in Xi'an, it would take approximately 25 years (in the year 2045) to reduce the annual PM2.5 level to 5 μg m−3, the new guideline value from World Health Organization. We also show that PM2.5 is primarily associated with anthropogenic emissions, which, underwent aqueous phase chemistry in winter and photochemical oxidation in summer as suggested by partial dependence of RH and Ox in different seasons. Therefore, reducing the anthropogenic secondary aerosol precursors at a higher rate, such as NOx and VOCs is expected to reduce the particulate pollution in this region more effectively than the current −3.3 % year−1 found in this study.

Thermo-electro-environmental analysis of a photovoltaic solar panel using machine learning and real-time data for smart and sustainable energy generation

The impact of meteorological parameters, including ambient temperature, wind velocity, ambient relative humidity, and solar radiation on photocurrent and thermal voltage of diode, as two main thermo-electrical parameters of a solar panel, is found. For this purpose, the experimental data obtained during a year, in addition to the post-processed images captured by an infrared thermal imaging camera, are used, and models for performance prediction by the artificial neural network are developed and validated. In addition, the impact of photocurrent and thermal voltage of diode on CO2 saving of the system is found. According to the results, for the investigated 320W polycrystalline panel, with 200.0% increase in the range of 500–1500 W m−2, solar radiation has the strongest impact on photocurrent. Moreover, the most effective meteorological parameter on the thermal voltage of diode is ambient temperature. Changing ambient temperature from 27 to 47 °C is accompanied by 9.36% growth in that parameter. The conducted sensitivity analysis also reveals that between photocurrent and thermal voltage of diode, the former is a more effective parameter on CO2 emission reduction of the system. When photocurrent values are 20% lower and 20% higher than the base case, the amounts of CO2 saving are 18.0% smaller and 14.6% greater, respectively. It means 32.6% variation within the range.

The impact of meteorological parameters on PM10 and visibility during the Middle Eastern dust storms.

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM10, AOD, air mass source origin, and visibility during severe desert dust storms (Average3h PM10 > 3200µgm-3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM10 mass concentration peak (137-553µgm-3) between 12:00-18:00 (local time) that was 18-24h before the dominant peak of the storm (3279-4899µgm-3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6h before the alarming PM10 peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3h around the time of the alarming PM10 peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM10 was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R2 = 0.95 is reported to estimate the maximum PM10 concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R2 = 0.86) with PM10 during periods with severe desert dust storms in the region.

Impact of Basic Parameters on Heat and Moisture Process Simulation of Bamboo Building Envelope in China (I): Meteorological Parameters

As a country of rich bamboo forest and large-scale bamboo processing industry, China is promoting the development of modified bamboo building materials and the bamboo construction systems. In the study on bamboo building envelope (BBE), as a hygroscopic construction, particular attention should be paid to the coupled heat and moisture (H&amp;M) process. However, the parameters basis and the thermal design code in China could not yet support such calculation on coupled H&amp;M process of BBE. In order to clarify the impact of meteorological parameters on the H&amp;M process of BBE, the study prepares 24 representative cities in China with climate data exported from the Meteonorm platform and 3 typical bamboos with complete material properties data tested through experiments, and then sets up comparison BBE model groups in the software WUFI Plus for annual H&amp;M process simulation. Statistical analysis clarifies the correlation law between the H&amp;M process of BBE and the meteorological parameters. The study highlights the importance of hygric meteorological data for the application of BBE.

A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

Motivated by factors such as the reduction in cost and the need for a shift towards achieving UN’s Sustainable Development Goals, PV (Photovoltaic) power generation is getting more attention in the cold regions of the Nordic countries and Canada. The cold climate and the albedo effect of snow in these regions present favorable operating conditions for PV cells and an opportunity to realize the seasonal matching of generation and consumption respectively. However, the erratic nature of PV brings a threat to the operation of the grid. PV power forecasting has been used as an economical solution to minimize and even overcome this limitation. This paper is therefore a comprehensive review of machine learning-based PV output power forecasting models in the literature in the context of Nordic climate. The impact of meteorological parameters and the soiling effect due to snow, which is unique to this climate, on the performance of a prediction model is discussed. PV power forecasting models in the literature are systematically classified into multiple groups and each group is analyzed and important suggestions are made for choosing a better model for these regions. Ensemble methods, optimization algorithms, time-series decomposition, and weather clustering are identified as important techniques that can be used to enhance performance. And notably, this work proposed two conceptual approaches that can be used to incorporate the effect of snow on PV power forecasting. Future research needs to focus on this area, which is crucial for the development of PV in these regions.

Assessing the impact of meteorological parameters for forecasting floods in the northern districts of Bihar using machine learning

Assessing the impact of meteorological parameters for forecasting floods in the northern districts of Bihar using machine learning

Impact of meteorological parameters on mosquito population abundance and distribution in a former malaria endemic area, central Iran

Meteorological parameters, have been identified as an important factor involved in the transmission of vector-borne diseases. Mosquitoes are extremely sensitive to weather conditions. The aim of this study was investigate the correlation between meteorological parameters and the abundance of mosquitoes in Kashan County. Mosquitoes were collected using four different traps, including hand catch, animal baited bed net trap (usually a cow), human baited bed net trap and BG-Sentinel trap with CO2 from May to December 2019. A total number of mosquitoes collected were 1756 out of which 1621 (92.31%) were Culex, 22 (1.25%) Culiseta and 113 (6.44%) Anopheles in nine species. Most mosquitoes were collected by BG-Sentinel trap with CO2 (63.78%). Monthly distribution of the mosquitoes indicated different monthly peaks. Their high density were recorded in September and were low in December. The spearman's correlation of the mosquito abundance and the meteorological parameters shows that correlation of the number of total collected mosquitoes with relative humidity and precipitation (Rainfall) was weak negative, and there was week correlation with wind speed, and positive strong correlation with temperature. Data collected with various trap types and mosquito correlation with meteorological parameters in this study can be used for mosquito surveillance and control programs. However, meteorological parameters affect the abundance of mosquitoes, but their impact is complex and most of these variables are species specific.

Population dynamics of defoliator and sucking pests in black gram

Population dynamics of black gram defoliators viz., leaf folder, Omiodes indicata (Fabricus), striped flea beetle, Phyllotreta striolata (Fabricus) and the sucking pest, bean aphids, Aphis craccivora (Koch) were studied in the entire crop growth period during Kharif 2020 and Rabi 2020 - 2021. The results exhibited the high peak of population of defoliator’s population at 43rd Standard Week (SW) (91 larvae/ 50 plants) and 14th SW (76 larvae/ 50 plants), 43rd SW (210 beetles/ 50 plants) and 14th SW (197 beetles/ 50 plants) during Kharif 2020 and Rabi 2020 – 2021, respectively. The sucking pest reached a high peak at 45th SW (325 aphids/ 50 plant) and 16th SW (305 aphids/ 50 plant) during Kharif 2020 and Rabi 2020 – 2021. Impact of meteorological parameters on the population dynamics of black gram pests were studied and interpreted.

Long-Term eBC Measurements with the Use of MAAP in the Polluted Urban Atmosphere (Poland)

In recent years, black carbon (BC) has been gaining more attention due to the diversity of anthropogenic sources and the harmful effects on human health, environment, and climate. In this paper, for the first time in Poland, the results of long-term measurements of eBC concentrations (2009–2020) at the urban background station in Zabrze (southern Poland) are presented. A Multi-Angle Absorption Photometer (MAAP) was used, which enables the measurement of eBC concentration in fine particulate matter (PM2.5). The mean concentration of eBC over the 11-year period (3.82 μg·m−3) was higher compared to the values recorded at most European urban stations. Annual averaged eBC levels showed a downward trend and clear seasonal variations, which was caused mainly by changes in the intensity of anthropogenic emissions. The impact of meteorological parameters, in particular air temperature and wind speed, which determine the intensity of emissions and the conditions of pollutant dispersion, was not without significance. The work additionally attempts to assess the possible impact of remedial actions carried out in Zabrze over the last decade. The results showed that modernization in industry and heating and maintenance of green areas potentially had the most important impact on the decline in eBC concentrations.

Impact of Meteorological Parameters on the COVID-19 Incidence: The Case of the City of Oran, Algeria

The current coronavirus disease 2019 (COVID-19) pandemic in the major countries of the world has become a serious threat to the health of all human beings. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. Among these parameters, we find the climatic factors that can play an important role in the spread of the COVID-19 epidemic. We have studied this phenomenon in the city of Oran in Algeria, which experienced its first case of COVID-19 on March 19, 2020. In this study, the main parameters, including the number of infected people with COVID-19, the average, minimum and maximum temperatures, the relative humidity rate and the wind intensity. A first analysis of the data with a Spearman rank correlation test did not yield significant results. Taking into account the average incubation period to adjust the data made it possible, during a second analysis, to show that the minimum temperature is significantly correlated with the new cases of COVID-19 in Oran city, but further studies over longer periods with additional parameters are needed to better understand this matter.

Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

Soil moisture is an important indicator for agricultural planting and agricultural water management. People have been trying to guide crop cultivation, formulate irrigation systems, and develop intelligent agriculture by knowing exactly what the soil moisture is in real time. This paper considers the impact of meteorological parameters on soil-moisture change and proposes a soil-moisture prediction method based on the Gene Expression Programming (GEP) algorithm. The prediction model is tested on datasets from Shunyi, Yanqing and Daxing agricultural farms, Beijing. The results show that the GEP model can predict soil moisture with a maximum correlation coefficient of 0.98, and the root-mean-square errors in three different farms were below 2.32.

Impact of meteorological parameters and population density on variants of SARS-CoV-2 and outcome of COVID-19 pandemic in Japan.

Although vaccines have become available, emergence and rapid transmission of new variants have added new paradigm in the coronavirus disease-2019 (COVID-19) pandemic. Weather, population and host immunity have been detected as the regulatory elements of COVID-19. This study aims to investigate the effects of weather, population and host factors on the outcome of COVID-19 and mutation frequency in Japan. Data were collected during January 2020 to February 2021. About 92% isolates were form GR clades. Variants 501Y.V1 (53%) and 452R.V1 (24%) were most prevalent in Japan. The strongest correlation was detected between fatalities and population density (rs = 0.81) followed by total population (rs = 0.72). Relative humidity had the highest correlation (rs = -0.71) with the case fatality rate. Cluster mutations namely N501Y (45%), E484K (30%), N439K (16%), K417N (6%) and T478I (3%) at spike protein have increased during January to February 2021. Above 90% fatality was detected in patients aged >60 years. The ratio of male to female patients of COVID-19 was 1.35:1. This study will help to understand the seasonality of COVID-19 and impact of weather on the outcome which will add knowledge to reduce the health burden of COVID-19 by the international organisations and policy makers.